The power of modern search engines is undeniable: you can summon knowledge from the internet at a moment's notice. There are many situations where search is relegated to strict keyword search, or when the objects aren't text, search may not be available. Furthermore, strict keyword search doesn't allow the user to search semantically, which means information is not as discoverable. Today, we share a reproducible, minimally viable product that illustrates how you can enable semantic search for arbitrary objects! Concretely, we will show you how to create a system that searches python code semantically -- but this approach can be generalized to other entities (such as pictures or sound clips).

Generating text is one of those projects that seems like a lot of fun to machine learning and NLP beginners, but one which is also pretty daunting. Or, at least it was for me. Thankfully, there are all sorts of great materials online for learning how RNNs can be used for generating text, ranging from the theoretical to the technically in-depth to those decidedly focused on the practical. There are also some very good posts which cover it all and are now considered canon in this space. All of these materials share one thing in particular: at some point along the way, you have to build and tune an RNN to do the work.

Artificial intelligence could be heading to Australian classrooms -- and in schools overseas, it's already there. In Bahia, Brazil, 15-year-old students David and Roama from Colegio Perfil often start their school day at home, or on the bus. They pick up their phones, log into the education app Geekie Lab, and begin their classes from wherever they are. "You can access it everywhere, as long as you have your phone with you," David said. Students from Colegio Perfil in Bahia use phones or computers to access the Geekie app.

This webinar covers a high-level introduction to data science and machine learning and their potential in a data-driven organization. Learn key trends and concepts, proven use cases and an overview of leading technologies.

Artificial intelligence could be heading to Australian classrooms -- and in schools overseas, it's already there. In Bahia, Brazil, 15-year-old students David and Roama from Colegio Perfil often start their school day at home, or on the bus. They pick up their phones, log into the education app Geekie Lab, and begin their classes from wherever they are. "You can access it everywhere, as long as you have your phone with you," David said. Students from Colegio Perfil in Bahia use phones or computers to access the Geekie app.

With the growth in the UAV industry, Mid Michigan Community College (MMCC) came with a plan to offer a 6-week course in drone training that teaches the basics of owning and using a drone. Topics such as how to fly a drone under the regulations prescribed by the FAA, and the opportunities for drone use in a commercial setting will be included in the class. "If you're a hobbyist and use the drones for recreation, you still have to have your drone registered by the FAA," said Robert Guliani, a photography professor at the MMCC. "Beyond just personal interest, there are a lot of individual types of businesses that can use this technology," said Scott Govitz, Associate VP of Workforce and Economic Development. "We wanted to kick off with this training for the general population, next, we'll look at offering some of these training options to the public," added Govitz.

We've told you about it, and now it's happening. The General Data Protection Regulation (GDPR) recently got approved. Are you and your organization ready to explain your models? Jo-Fai Chow from H2O will lead us in a journey through the construction and interpretation of Machine Learning models, in an hand-on experience open to everybody (previous knowledge of ML is not required). We will discover the use of two R packages, h2o & LIME, for automatic and interpretable machine learning.

An artificial agent for financial risk and returns' prediction is built with a modular cognitive system comprised of interconnected recurrent neural networks, such that the agent learns to predict the financial returns, and learns to predict the squared deviation around these predicted returns. These two expectations are used to build a volatility-sensitive interval prediction for financial returns, which is evaluated on three major financial indices and shown to be able to predict financial returns with higher than 80% success rate in interval prediction in both training and testing, raising into question the Efficient Market Hypothesis. The agent is introduced as an example of a class of artificial intelligent systems that are equipped with a Modular Networked Learning cognitive system, defined as an integrated networked system of machine learning modules, where each module constitutes a functional unit that is trained for a given specific task that solves a subproblem of a complex main problem expressed as a network of linked subproblems. In the case of neural networks, these systems function as a form of an "artificial brain", where each module is like a specialized brain region comprised of a neural network with a specific architecture.

Education is moving away from traditional rows of students looking at the same textbook while a teacher lectures from the front of the room. Today's classrooms are not simply evolving to use more technology and digital resources; they are also investing in machine learning. Machine learning is defined as "a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed." For example, in education, we see machine learning in learning analytics and artificial intelligence. Machine learning is essentially mining data.

In the past year there's been a bit of a careers and job scare when it comes to artificial intelligence, automation, and related technologies. Big consulting firms have conducted studies about the future of jobs and whether they will be lost to artificial intelligence. The consensus is that jobs will be lost, while some jobs will be created. "The development of automation enabled by technologies including robotics and artificial intelligence brings the promise of higher productivity (and with productivity, economic growth), increased efficiencies, safety, and convenience," said McKinsey in a study released last year. "But these technologies also raise difficult questions about the broader impact of automation on jobs, skills, wages, and the nature of work itself."